Accuracy of three-dimensional virtual simulation of the soft tissues of the face in OrtogOnBlender for correction of class II dentofacial deformities: an uncontrolled experimental case-series study

Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery

Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

BACKGROUND

The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.

OBJECTIVES

To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT.

DESIGN

Systematic review.

DATA SOURCES

Unrestricted search of indexed databases and reference lists of included studies.

DATA SELECTION

Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included.

DATA EXTRACTION

Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively.

DATA SYNTHESIS

Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB.

LIMITATIONS

Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment.

CONCLUSION

AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT.

REGISTRATION

PROSPERO CRD42022366864.

The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review

Three-dimensional soft tissue simulation has become a popular tool in the process of virtual orthognathic surgery planning and patient-surgeon communication. To apply 3D soft tissue simulation software in routine clinical practice, both qualitative and quantitative validation of its accuracy are required. The objective of this study was to systematically review the literature on the accuracy of 3D soft tissue simulation in orthognathic surgery. The Web of Science, PubMed, Cochrane, and Embase databases were consulted for the literature search. The systematic review (SR) was conducted according to the PRISMA statement, and 40 articles fulfilled the inclusion and exclusion criteria. The Quadas-2 tool was used for the risk of bias assessment for selected studies. A mean error varying from 0.27 mm to 2.9 mm for 3D soft tissue simulations for the whole face was reported. In the studies evaluating 3D soft tissue simulation accuracy after a Le Fort I osteotomy only, the upper lip and paranasal regions were reported to have the largest error, while after an isolated bilateral sagittal split osteotomy, the largest error was reported for the lower lip and chin regions. In the studies evaluating simulation after bimaxillary osteotomy with or without genioplasty, the highest inaccuracy was reported at the level of the lips, predominantly the lower lip, chin, and, sometimes, the paranasal regions. Due to the variability in the study designs and analysis methods, a direct comparison was not possible. Therefore, based on the results of this SR, guidelines to systematize the workflow for evaluating the accuracy of 3D soft tissue simulations in orthognathic surgery in future studies are proposed.

Development and validation of a novel craniofacial statistical shape model for the virtual reconstruction of bilateral maxillary defects

Bilateral maxillary defects are a challenge for fibula free flap reconstruction (FFFR) surgery due to limitations in virtual surgical planning (VSP) workflows. While meshes of unilateral defects can be mirrored to virtually reconstruct missing anatomy, Brown class c and d defects lack a contralateral reference and associated anatomical landmarks. This often results in poor placement of osteotomized fibula segments. This study was performed to improve the VSP workflow for FFFR using statistical shape modeling (SSM) - a form of unsupervised machine learning - to virtually reconstruct premorbid anatomy in an automated, reproducible, and patient-specific manner. A training set of 112 computed tomography scans was sourced from an imaging database by stratified random sampling. The craniofacial skeletons were segmented, aligned, and processed via principal component analysis. Reconstruction performance was validated on a set of 45 unseen skulls containing various digitally generated defects (Brown class IIa-d). Validation metrics demonstrated promising accuracy: mean 95th percentile Hausdorff distance of 5.47 ± 2.39 mm, mean volumetric Dice coefficient of 48.8 ± 14.5%, compactness of 7.28 × 105 mm2, specificity of 1.18 mm, and generality of 8.12 × 10-6 mm. SSM-guided VSP will allow surgeons to create patient-centric treatment plans, increasing FFFR accuracy, reducing complications, and improving postoperative outcomes.

A Quantitative and Qualitative Clinical Validation of Soft Tissue Simulation for Orthognathic Surgery Planning

The purpose of this study was to perform a quantitative and qualitative validation of a soft tissue simulation pipeline for orthognathic surgery planning, necessary for clinical use. Simulation results were retrospectively obtained in 10 patients who underwent orthognathic surgery. Quantitatively, error was measured at 9 anatomical landmarks for each patient and different types of comparative analysis were performed considering two mesh resolutions, clinically accepted error, simulation time and error measured by means of percentage of the whole surface. Qualitatively, evaluation and binary questions were asked to two surgeons, both before and after seeing the actual surgical outcome, and their answers were compared. Finally, the quantitative and qualitative results were compared to check if these two types of validation are correlated. The quantitative results were accurate, with greater errors corresponding to gonions and lower lip. Qualitatively, surgeons answered similarly mostly and their evaluations improved when seeing the actual outcome of the surgery. The quantitative validation was not correlated to the qualitative validation. In this study, quantitative and qualitative validations were performed and compared, and the need to carry out both types of analysis in validation studies of soft tissue simulation software for orthognathic surgery planning was proved.

The Facial Reconstruction of a Mesolithic Dog, Muge, Portugal

This paper presents the facial reconstruction of a Mesolithic dog whose skeleton was recovered from the Muge shell middens (Portugal) in the 19th century. We used the anatomical deformation approach based on a collection of computer tomography images as an attempt to reconstruct the Muge dog’s head appearance. We faced a few challenges due to the level of bone displacement and the absence of some cranium anatomical parts, as well as accurate information on soft tissue thickness for modern dogs. This multidisciplinary study combined anatomical, veterinary, zooarchaeological, artistic and graphic aspects to allow for the facial reconstruction of the Muge dog. Albeit an approximation, it confers a recognition to this prehistoric finding.

Psychosocial changes in patients submitted to orthodontic surgery treatment: a systematic review and meta-analysis

OBJECTIVE

This systematic review aims to answer the following question: What is the psychological impact of orthognathic surgery on patients with dentofacial deformities undergoing orthodontic-surgical treatment?

MATERIAL AND METHODS

The search was adapted for each of the following databases: American and Caribbean Center on Health Sciences (LILACS), Cochrane Library, Embase, Psychinfo, PubMed/Medline, Scopus and Web of Science, and gray literature using Google Scholar, OpenGrey, and ProQuest. The risk of bias was assessed using the Joanna Briggs Institute Critical Assessment Checklist. This study performed estimates of interest, random-effects meta-analyses, and calculated heterogeneity using Higgins inconsistency index (I²).

RESULTS

A total of 6751 references were found in all searches. After applying the eligibility criteria after full-text reading, 37 studies comprised the final qualitative synthesis. Thirteen studies were included in quantitative synthesis, and it was possible to meta-analyze data from the following questionnaires: GHQ-28, MMPI, RSES, and SCL-90-R. There was an improvement in psychological aspects related to depression, hysteria, self-esteem, anxiety, obsessive-compulsiveness, interpersonal sensitivity, paranoid ideas, and psychoticism (p < 0.05).

CONCLUSIONS

Correction of dentofacial deformity through orthodontic-surgical treatment is associated with improvements observed in several psychological domains, especially in relation to depressive states.

CLINICAL RELEVANCE

This result highlights the importance of surgeons and orthodontists in promoting adequate control of patients' expectations and treatment goals taking into account the individual's psychological aspects.